[论文解读] A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study

Halgurd S. Maghdid, Kayhan Zrar Ghafoor+4 more|arXiv (Cornell University)|Mar 16, 2020

COVID-19 diagnosis using AI被引用 70

一句话总结

本论文提出一个利用 AI 的框架，使用智能手机嵌入传感器来检测 COVID-19 并预测肺炎严重程度，作为设计研究。

ABSTRACT

Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Nowadays Smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.

研究动机与目标

• 以普及的智能手机硬件为基础，推动低成本替代传统 COVID-19 诊断的方法。
• 提出一个 AI 驱动的框架，从智能手机读取传感器测量值，以预测疾病存在与肺炎严重程度。
• 强调基于智能手机的诊断在大流行应对中的可行性、设计考虑因素与潜在收益。

提出的方法

• 利用智能手机内置传感器（摄像头、麦克风、温度、惯性、距离、颜色、湿度、无线）来捕捉与健康相关的信号。
• 开发一个 AI 启用的框架，处理传感器数据以预测 COVID-19 状态和肺炎严重程度。
• 主张一种低成本、可被普通人通过智能手机使用的可访问解决方案。

实验结果

研究问题

• RQ1 智能手机嵌入传感器是否能提供用于 COVID-19 诊断的判别信号？
• RQ2 该框架能否从传感器数据预测与 COVID-19 相关的肺炎严重程度？
• RQ3 在移动设备上部署 AI 以检测传染病的设计考虑因素与可行性挑战是什么？

主要发现

• 该工作提出一个设计研究，框定基于智能手机的低成本 COVID-19 检测方法。
• 该框架从智能手机读取多传感器信号以预测疾病结局和肺炎严重程度。
• 它讨论了在消费设备上部署 AI 启用诊断的潜在收益和局限性。